作物产量指数设计及在黑龙江省的应用

针对传统基于归一化植被指数（normalized difference vegetation index,NDVI）的作物长势监测方法对于同一时期处于不同生育阶段的作物缺乏可比性,以及NDVI的高低不能直接代表产量的高低的问题,该研究设计了一种可以动态反映作物长势和产量变化的具有时空可比性的统计指数（作物产量指数）。以黑龙江为例,基于单产数据、气象数据、遥感数据、作物分布数据,对3种作物分别进行估产分区,综合使用随机森林重要性评价方法和留一法为各估产分区筛选最优估产建模变量,构建动态估产模型和产量指数计算模型,并在2022年作物生长季（6—9月）进行了大豆、玉米、水稻的动态估产和产量指数预报和分析。结果显示：1）建模指标的重要性从高到低依次为趋势单产、遥感植被指数、气候类指标。2）3种作物整体单产预测精度最高的为水稻,其平均绝对相对精度(mean absolute relative precision,MARP)为95.20%,其次是玉米（MARP为93.81%）,最后是大豆（MARP为92.73%）。3）以历史5 a为基期计算的3种作物的6—9月的产量指数的对比结果显示,大部分区县3种作物各月的产量指数差异处于“平”状态。4）生长季产量指数的月环比结果显示大部分区县产量指数的月环比值处于-0.01~0.01之间。该研究设计的作物产量指数可用于比较某一统计单元（如县、市或省）在特定评估时间点相对于其历史平均单产的增减状况,也可以环比相邻两个评估时间点的产量变化情况;在空间维上可以比较同处于某个特定评估时间点的不同统计单元的单产指数的高低情况,在长势监测、产量预报等中具有很好的应用前景。

Applying crops yield index in Heilongjiang Province of China

Real-time and accurate assessment of crop growth and yield is of great significance for grain macro-control measures, crop insurance, and agricultural production planning. However, it is lacking in the comparability for the crops in different growth stages at the same period during traditional monitoring crop growth using normalized difference vegetation index (NDVI). The high/low NDVI value cannot directly represent the high/low yield. In this study, a spatiotemporal comparable statistical index (crop yield index) was designed to dynamically represent the changes in crop growth and yield. The yield, meteorological, remote sensing and crop distribution data were first collected from the Heilongjiang Province of China. The yield estimation zoning was carried out for three crops (maize, rice, and soybeans) by spatial "K" luster analysis by tree edge removal (SKATER). The optimal variables of yield estimation were screened for each yield estimation zone using the random forest importance evaluation and Leave-One-Out. Secondly, the yield estimation models were built and validated for each yield estimation zoning. After that, the yield of three crops was estimated monthly during the growing season of 2022 (June-September), according to the yield estimation models. Taking the historical five years as the base period, the maximum indexation was used to index the predicted yield to obtain the crop yield index. Finally, the monthly analysis was implemented to determine the changes in the yield index that related to the historical reference period and the previous month during the growing season of 2022. The results show that: 1) The whole province was divided into 3, 3, and 4 yield estimation zones for rice, soybean, and maize, respectively. 2) The importance of modeling indicators was ranked in the descending order of the trend yield, remote sensing vegetation, and climate indicators. 3) The importance of each candidate modeling variable was varied in the different yield estimation zones. The different optimal modeling variables were also selected for each yield estimation zone. In the yield estimation models using the optimal modeling variables, the yield prediction accuracy of rice was the highest, with a mean absolute relative precision (MARP) of 95.20%, followed by maize (MARP=93.81%), and finally soybean (MARP=92.73%). 4) The yield indices of three crops from June to September were compared with the calculated using a historical five-year period. There were "flat" state differences in the yield indices of the three crops in most counties (i.e., the difference in the yield index was within 0.05). 5)The monthly yield index differences (the current month''s yield index minus the previous month''s yield index) of the three crops in most counties are between −0.01 and 0.01. The crop yield index shared excellent spatiotemporal comparability. On the temporal dimension, the crop yield index can be expected to compare the increase or decrease of a certain statistical unit (such as a county, city, or province) that is related to the historical average yield at a specific evaluation time point, or to compare the yield changes of two adjacent evaluation time points. On the spatial dimension, the crop yield index can be compared with the different statistical units at a specific evaluation time point. This crop yield index can provide promising application prospects in growth monitoring and yield forecasting.